Instructions to use inflatebot/MN-12B-Mag-Mell-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inflatebot/MN-12B-Mag-Mell-R1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inflatebot/MN-12B-Mag-Mell-R1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("inflatebot/MN-12B-Mag-Mell-R1") model = AutoModelForCausalLM.from_pretrained("inflatebot/MN-12B-Mag-Mell-R1") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use inflatebot/MN-12B-Mag-Mell-R1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inflatebot/MN-12B-Mag-Mell-R1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inflatebot/MN-12B-Mag-Mell-R1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/inflatebot/MN-12B-Mag-Mell-R1
- SGLang
How to use inflatebot/MN-12B-Mag-Mell-R1 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "inflatebot/MN-12B-Mag-Mell-R1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inflatebot/MN-12B-Mag-Mell-R1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "inflatebot/MN-12B-Mag-Mell-R1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inflatebot/MN-12B-Mag-Mell-R1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use inflatebot/MN-12B-Mag-Mell-R1 with Docker Model Runner:
docker model run hf.co/inflatebot/MN-12B-Mag-Mell-R1
Thai language support?
Does this model support Thai language?
That's a great question!
There wasn't any Thai data in the finetuned components of Mag Mell, so it wouldn't be any better than Mistral Nemo in Thai language tasks.
Mistral Nemo was trained on English, French, German, Spanish, Italian, Portuguese, Russian, Chinese and Japanese. So unfortunately, it (and this model) won't have support for Thai.
I'm not sure I know of any models for the Thai language! What's your use case, if you don't mind me asking?
I had a look around, and it looks like the Llama 3.1, 3.2 and 3.3-based models all have support for Thai language, so any finetune based on those models might do what you're looking for.
if i want to use this model, i need to use english instead of thai language right?
That's correct, sorry to say. If I see any RP/writing models specifically for Thai language I'll put them in this thread.